HPIC: The Habitable Worlds Observatory Preliminary Input Catalog

To construct an input catalog for future space-based direct imaging missions such as HWO, one does not need to start from scratch. Direct imaging of Earth-sized planets is feasible only for the population of bright nearby stars, and such stars have been surveyed by several past and ongoing missions. In particular, the target lists for the TESS and Gaia missions are useful starting points when constructing an input catalog for HWO. The TESS Input Catalog (TIC) contains the observed and derived properties as well as cross-matching information for the list of 1.7 billion objects used to plan the TESS mission's survey for transiting exoplanets (Ricker et al. 2015; Stassun et al. 2018, 2019; STScI 2018; Paegert et al. 2021). Gaia Data Release 3 (DR3) contains astrometric data for 1.8 billion objects and includes precise measurements of parallaxes, distances, proper motions, and photometry, as well as astrophysical properties derived by multiple automated pipelines (Prusti et al. 2016; Gaia Collaboration et al. 2023).

Together, with over a billion stars, the TESS and Gaia target lists are much too large for a direct imaging survey. To reduce the catalog size and omit stars that are not feasible targets for any direct imaging mission within the trade space, we make two reasonable cuts to the catalog. First, we implement a distance cutoff of 50 pc, beyond which the angular size of the habitable zone will fall within the coronagraph's inner working angle. At 50 pc, a planet at 1 au would have an angular separation of just 20 mas. Even for an idealized coronagraph with a 1λ/D inner working angle, an 8 m diameter telescope operating at 1 μm would be limited to separations greater than 26 mas. Second, we apply an apparent magnitude cutoff to exclude sources that are too faint and would require unrealistically long exposure times to detect planets in reflected light. Though trimming the catalog at 8th magnitude would likely contain all targets for which exo-Earth direct imaging would be feasible, the HPIC is designed to extend to fainter magnitudes to allow for surveys of other planet types and to account for mission architectures spanning the range of the trade space. We adopt a 12th magnitude cutoff, the faintest our catalog can extend to while still ensuring that the catalog is volume complete. As shown in the histogram of magnitudes of TIC objects in Figure 1, the number of objects begins to decrease at T magnitudes of ∼12.5, indicating that beyond that magnitude, the TIC is no longer volume complete.

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Whereas previous catalogs of direct imaging target stars were largely developed via manual curation, for the HPIC we have created a pipeline to automate our target list generation. This will allow for simple updates as additional data sets are released in the future, and there will be no ambiguity on how stars were selected and how their stellar properties were calculated. Here we detail the procedure for constructing our target list and the pipeline we have developed (illustrated in Figure 2).

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To form the foundation of the HPIC, we start with the target lists of the TESS and Gaia missions as described in the previous section. Our first step is to query the TIC 8.2, hosted on the Strasbourg Astronomical Data Center (CDS) VizieR service (Ochsenbein et al. 2000; STScI 2018), for all objects with distances less than 50 pc. Selecting all nearby TIC objects with distances less than 50 pc yields the majority of stars that are good candidates for direct imaging, but there are a few hundred objects in the TIC without distances that may still be good targets, such as binaries without accurate parallaxes. To make sure we include these objects in our list, we also include all bright TIC objects, with or without distance measurements. While ideally we could obtain the brightest stars just by selecting based on TESS magnitude, we are faced with the problem that a few dozen of the brightest TIC objects appear to have incorrect estimates of their T magnitudes. This is due to the fact that T magnitudes are often calculated using Gaia photometry, but the brightest objects were too bright to be observed by the Gaia mission, and their T magnitudes may be erroneously computed using a faint background Gaia source. In these cases, we select objects based on their Two Micron All Sky Survey (2MASS) J magnitudes instead of incorrect T magnitudes. To obtain the brightest TIC objects, we make two separate queries: we select objects with T < 8 to obtain the vast majority of bright stars, and we select objects with J < 4 to ensure the very brightest objects without accurate T magnitudes are included in our catalog.

We now have two subsets of the TIC: all the objects with distances less than 50 pc and all the brightest TIC objects (T < 8 or J < 4). We then take the union of these two subsets of the TIC, removing objects with duplicate TIC identifiers. We remove objects in the resulting combined list with distances in the TIC greater than 50 pc but keep objects that do not have distances provided. In Section 2.3.4, we discuss how we obtain distance estimates for these objects and how objects are removed from our catalog if their newly obtained distance exceeds 50 pc.

Apart from the bright and nearby objects in the TIC, there are several hundred nearby objects observed by Gaia DR3 that are not included in the TIC. Most of these are fainter field stars and newly resolved binaries. To identify these objects, we first query the Bailer-Jones et al. (2021) catalog of Bayesian distance estimates for Gaia DR3 sources, selecting all objects with distances less than 50 pc. Within this list of nearby Gaia objects there are objects that are not in the TIC, and to identify them, we need to remove objects that are redundant with TIC sources.

To find which objects are the same and remove duplicates, we identify the corresponding Gaia DR3 identifiers for all objects in our subset of the TIC. The TIC was constructed using Gaia DR2 but has not yet been updated for Gaia DR3 (Stassun et al. 2019; Paegert et al. 2021). As such, it includes the Gaia DR2 identifiers for most objects but does not contain Gaia DR3 identifiers. ⁴ Fortunately, the Gaia team has done extensive cross-matching between DR2 and DR3 and includes these results in the dr2_neighborhood table in the Gaia Archive (Torra et al. 2021; Gaia Collaboration et al. 2023). Using this table for every object in the TIC with a Gaia DR2 identifier, we select the DR3 object that is the closest match in terms of both position and G magnitude. For TIC objects without Gaia DR2 identifiers, we perform a positional cross-match with Gaia DR3 using the CDS X-Match service (Boch et al. 2012). For the list of Gaia DR3 sources within 10'' of an object, we select the one that is closest in terms of angular separation and TESS magnitude. Finally, for Gaia DR3 objects that lack a corresponding object in the TIC, we use the same DR2-DR3 neighborhood table as we have discussed earlier to find DR2 identifiers corresponding to each object. This is necessary as many external catalogs we use in later sections identify sources based on their identifiers in Gaia DR2.

At this point, we now have two lists: a subset of the nearest and brightest objects in the TIC and all nearby Gaia DR3 objects. We take the union of these two target lists, joining on Gaia DR3 IDs and removing objects with duplicate IDs. Joining the TESS and Gaia lists for nearby and bright objects yields several tens of thousands of sources, many of which are very faint and poorly characterized. In order to narrow this list of objects down to the sources that will make good candidates for space-based direct imaging, we apply the magnitude cut discussed in Section 2.1 to only include objects with TESS T and Gaia G magnitudes less than 12.

Applying these cuts to the target list reduces our target list to a manageable size and restricts it to the population of nearby bright stars, of which the majority have more observations and are better characterized than the average field star. For our combined list of nearby bright TIC and Gaia DR3 objects, we gather measurements for their observable properties and estimates for their derived stellar properties. For every object with a Gaia DR3 identifier, we query the Gaia Archive to obtain the full table for Gaia DR3 data and astrophysical properties calculated via the Gaia Apsis pipeline (Creevey et al. 2023; Fouesneau et al. 2023; Gaia Collaboration et al. 2023). We similarly gather the full TIC 8.2 entries for every object in our list with a TIC ID (Paegert et al. 2021). Using this information, we further refine our target list by removing stars that the TIC identifies as artifacts or duplicate sources with other TIC objects, as well as removing objects that are tagged as galaxies. We then query the CDS Simbad database for each object based on their primary identifier in their source catalog, namely, their TESS or Gaia DR3 ID (Wenger et al. 2000). For the subset of objects where the identifier is not found by Simbad, we perform a second query using an alternative identifier such as Hipparcos, 2MASS, Tycho, or Gaia DR2 IDs. From Simbad, we obtain additional identifiers not in the TIC as well as photometry, spectral types, spectroscopic properties, and the binarity of objects. In addition to the catalogs listed previously, we also use VizieR to gather several other tables for stellar measurements and derived properties (Ochsenbein et al. 2000).

For every star in the HPIC, we aim to obtain maximum completeness in the astrophysical properties necessary for yield calculations while also ensuring the reliability and self-consistency of the properties. Measurements of these properties are often available from multiple sources, each with their own systematic uncertainties. In this section, we describe our procedure for prioritizing different sources of estimates and observations for each property.

2.3.1. Positions and Proper Motions

2.3.2. Photometry

2.3.3. Binarity and Contaminants

2.3.4. Distances

2.3.5. Effective Temperature

2.3.6. Luminosity

2.3.7. Radius

2.3.8. Metallicity

2.3.9. Mass

2.3.10. Age

2.3.11. Surface Gravity

The results of our automated pipeline to gather and compute stellar properties for stars in the HPIC are shown in Table 2 and in the histograms shown in Figure 3. The HPIC contains 12,944 objects, and for each of these objects, we aim for the highest possible catalog completeness in astrophysical properties and photometry. Note that in this section we are referring to "catalog completeness" in terms of the fraction of stars in our catalog with a given property rather than "survey completeness," which we will discuss in Section 3. We can see that for most astrophysical properties (excluding ages), the catalog completeness is on the order of 98% or higher. Many of the objects lacking fundamental properties fall in a few main categories. There are binaries that do not have accurate parallax measurements, newly resolved binary companions that have not been studied in detail, or distant field objects lacking distance measurements (thus causing them to remain in our target list when they should have been removed). Excluding this 2% of objects from our target list is unlikely to have noticeable effects on yield calculations, but we choose to keep these objects so that their properties can be updated with the results of future studies prior to HWO. Of the various astrophysical properties that we gather in our pipeline, it is unsurprising that stellar age has much lower catalog completeness due to the difficulty in obtaining age estimates (see Section 2.3.10). Other than ages, [Fe/H] measurements have slightly lower catalog completeness than the other astrophysical properties listed. This is because determining [Fe/H] typically requires analyses of host star spectra, which may not be available for lesser-studied target stars.

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In terms of photometry, all stars in our catalog have photometry in multiple bands, but they differ in which bands are available. Most objects do not have photometry in all of the bands we gathered measurements for, but some bands, such as Johnson B and V, 2MASS JHK_s , and the Gaia G bands, have close to 100% completeness. The TESS T band has 100% completeness in our catalog, but these values are calculated estimates rather than measurements. Other bands, namely, the U, R, and I bands, are only available for a limited sample of stars in our catalog due the fact that either the stars have not been surveyed in these bands or, in the case of the U band, the objects were too faint to be detected at the given wavelength.

Now we assess the degree to which our automated pipeline provides values for each of the stellar measurements that are reliable, precise, and consistent with other literature sources. In order to do this, we compare the properties gathered and calculated in the HPIC to those of a trusted source that was not directly drawn from in the creation of our target list. We adopt the NASA ExEP Mission Star List for HWO as our baseline for comparison (Mamajek & Stapelfeldt 2024). As mentioned earlier, this list of the 164 best targets for space-based direct imaging is curated by hand and contains the most heavily vetted measurements for the properties of these stars.

Figure 4 shows the comparison of the values of astrophysical properties between the two target lists. All of the stars in the ExEP list are contained within the HPIC and are shown in this figure. As we have two separate estimates for log(g) in our catalog, we used the procedure described in Section 2.3.11 to obtain a best estimate of log(g) on a per-star basis in order to compare to the values in the ExEP list. We use spectroscopic log(g) values when available and otherwise use bulk values. In Figure 5, we plot the fractional residuals between the two lists. Note that for the values of log(L), [Fe/H], and log(g), we plot the differences rather than fractional differences to avoid zeros in the denominator. Looking at these plots we can see that, for the most part, the values for each parameter adhere closely to a one-to-one correlation between both target lists. For some properties there is a clustering of residuals around 0, as in some cases the same source was used to gather the value of an astrophysical property in both the HPIC and ExEP lists. This is most apparent for distances, as both catalogs primarily use distances from Gaia DR3 parallaxes. We notice that for a few lower-mass stars there tends to be a small discrepancy in T_eff and log(L), likely caused by the fact that these stars may not be as well characterized or may be more difficult to characterize than other stars, resulting in greater variation in measured properties between sources. This discrepancy propagates to our values of stellar radii, which for lower-radius stars are slightly smaller in the HPIC than in the ExEP list. Still, accounting for the uncertainties in radius values, most of these discrepant values are still consistent with the ExEP value within 1σ–2σ.

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There is a larger spread in stellar mass values between the two catalogs, but such is to be expected, as stellar masses tend to be more difficult to constrain than other stellar properties, so one would expect larger variability between different estimates. The values for stellar masses are consistent to within around 10%, which is notable, as the uncertainties on individual estimates often exceed that. A 10% precision in mass is more than sufficiently precise for use in yield calculations, as the primary use of stellar masses is to derive planetary orbital periods that go as M^−0.5 for a given semimajor axis and are thus less sensitive to uncertainties in mass. Mass estimates in the ExEP list tend to be slightly higher than those in the HPIC, likely representing systematic differences in the means by which masses were acquired. The values of [Fe/H] and log(g) appear to exhibit larger scatter from a one-to-one correlation in Figure 4. However, given the larger uncertainties in these properties, the values in both lists remain consistent with each other except for the case of one or two outliers.

The HPIC contains the stellar properties necessary for exoplanet yield calculations, as well as additional parameters that are useful to characterize exoplanet host stars. A list of the columns included in the HPIC can be found in Appendix A, while a full list of the source and quality flags can be found in Appendix B. The HPIC is publicly available and hosted by the NASA Exoplanet Archive: doi:10.26133/NEA39 (Tuchow et al. 2024).

The HPIC is not the final input catalog for HWO; rather, it serves as the most complete list so far of potential targets for space-based direct imaging with HWO. The HPIC represents the state of our current knowledge about the stellar properties of the population of bright and nearby stars. However, our knowledge of stellar properties is constantly evolving, and in advance of HWO's launch in the 2040s, we expect to learn more about these target stars. Current missions like TESS and Gaia, as well as upcoming missions such as PLATO, will help us to better constrain the stellar properties of these stars and identify new planet hosts (Ricker et al. 2015; Prusti et al. 2016; Miglio et al. 2017). As our knowledge of these stars is continually improving, we intend to keep the HPIC regularly updated with the results of new studies such as future Gaia data releases. In future updates of the HPIC, we would like to add measurements of additional astrophysical properties, such as those that NASA's ExoPAG SAG 22 identified as being most useful to the astronomical community (Hinkel et al. 2021). For example, we like to obtain stellar properties including stellar activity indicators, disk properties, full binary orbits, and X-ray and UV fluxes. Additionally, one of the most useful but also difficult to obtain stellar properties is the age of a star. In the HPIC, we currently have stellar ages listed when obtained from the same source as the stellar mass, but in future updates we would like to increase our completeness in stellar ages. This would involve gathering age estimates from a wide variety of sources, such as ages obtained via asteroseismology or gyrochronology. While it will not be feasible to obtain these different stellar property measurements for all stars in our sample, in future releases of the HPIC we would like to determine which stars have measurements of certain less commonly obtained properties.

First, we examine the impact of the HPIC on exo-Earth candidate (EEC) yields. To do so, we perform two identical yield calculations, one using the AYO input catalog used by Stark et al. (2019) and one using the HPIC. For both calculations, we simulate a 2 yr EEC survey using the LUVOIR-B-like scenario detailed in Stark et al. (2019). Briefly, this consists of a 6.7 m inscribed diameter telescope with parallel UV and visible wavelength coronagraph channels using deformable mirror-assisted vortex charge 6 coronagraphs. We make identical astrophysical, mission, and survey assumptions to those made in Stark et al. (2019), with three exceptions. We increase the spectral resolution for characterization from R = 70 to R = 140 to be consistent with the recent results of Latouf et al. (2023). This change alone would lower the expected EEC yield by ∼10%. However, we make two additional changes that recover this yield loss. First, because we are interested in how the target selection changes with the input stellar catalog, we adopt identical exozodi levels for all stars (three zodis) instead of randomly drawing from a distribution. Second, we include some stars from the original AYO catalog that were excluded from previous calculations. The original AYO input catalog of Stark et al. (2019) cut all stars without spectral classifications in an effort to avoid spurious sources. However, using the HPIC, we are able to verify that almost all of these stars are indeed valid sources. Therefore, to ensure a valid comparison, we allow AYO catalog entries without stellar classifications.

To simulate the EEC survey, we distribute planets over the Kopparapu et al. (2013) optimistic habitable zone, ranging from 0.95 to 1.67 au for a solar twin. We distribute planet radii from 0.6 to 1.4 Earth radii with a stellar insolation-dependent lower limit, consistent with the EEC definition established by the LUVOIR and HabEx studies (The LUVOIR Team 2019; Gaudi et al. 2020). We adopt the occurrence rates of Dulz et al. (2020), such that η_⊕ = 0.24, and sample all possible orbits and phases. We use the AYO method detailed in Stark et al. (2019) to optimize target selection and exposure times in order to maximize the expected yield of the mission.

The results of our yield calculations with the original AYO input catalog and the HPIC are listed in Table 3. For our baseline case using the original AYO input catalog, we estimate an EEC yield of 29.07 with 215 stars selected for observation. Using the updated HPIC, the yield decreases negligibly to 28.48 with 211 stars selected for observation. The population of stars selected for each survey is shown in Figure 6, color coded by HZ completeness. Among these objects are the best candidates for exoplanet direct imaging identified by the ExEP mission stars list.

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Eleven of the 211 stars selected from the HPIC are newly added stars that do not appear in the original AYO output target list. These newly added stars mostly serve to replace stars from the previous list that were no longer selected, mainly due to revisions in their stellar properties or newly discovered binary companions that cause stray light and increased exposure times. A few of the 211 stars selected from the HPIC were available in the original AYO stellar catalog but went unselected in our baseline calculation. These stars were selected either because new estimates of their properties made them better options or simply because they were filling the spots left behind by objects that decreased in habitable zone completeness. Many of the selected stars were the same between both calculations but changed in expected HZ completeness due to revisions to their properties. We conclude that updating the quality and comprehensiveness of the target list does not have a significant impact on the total expected EEC yield but does affect which individual stars are selected, as well as their exposure times and expected completeness.

We do not yet know the design details of the HWO mission. One of the motivations for broadening the input catalog and extending the completeness and accuracy of stellar properties is to enable accurate yield calculations for a wide variety of telescope designs. Mission architectures that differ substantially from the HabEx and LUVOIR concepts could have significant differences in the population of stars they are able to observe.

To demonstrate the utility of the HPIC for studying missions that deviate from the LUVOIR-B baseline, we consider a larger telescope with higher throughput than LUVOIR-B. We refer to this hypothetical scenario as a "Super LUVOIR-B." Specifically, we choose an inscribed diameter of 8 m (compared to the 6.7 m inscribed diameter of LUVOIR-B) and increase the end-to-end throughput by a factor of 4. This factor of 4 in throughput serves to represent plausible reductions in exposure times from tangible design trades, such as a reduction in aluminum reflections, parallelized dual visible wavelength channels, improved coronagraph design, and/or improved point-spread function calibration techniques. Using this telescope design, we again simulate the EEC yield using the HPIC.

Figure 7 and Table 3 show the results of the "Super LUVOIR-B" yield calculation. The EEC yield increases by more than a factor of 2 when compared to the baseline LUVOIR-B yield calculation. Importantly, this increase in yield comes in large part from an expanded target list, more than twice the size of the baseline target list. Comparing the "Updated Target List" plot in Figure 6 with Figure 7, one can see that the selected target list extends to more distant stars.

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A few objects from the "Updated Target List" case were not selected as targets in the "Super LUVOIR-B" case. These objects, mainly close-by stars with high stellar luminosities and low HZ completeness, appear to not have been included in the "Super LUVOIR-B" list of targets because better candidates at larger distances were made available by the larger telescope diameter and throughput. Since this survey has more choice in potential direct imaging targets, it does not need to settle for these lower completeness targets.

Studies of space-based direct imaging mission concepts have primarily focused on the detection of Earth-like planets (Brown 2005; Savransky et al. 2010; Stark et al. 2014; Morgan et al. 2019), and for good reason—this is a primary science motivator that drives mission design. However, this is not the only exoplanet science that could result from HWO; a mission designed to characterize potentially Earth-like planets could excel at detecting Jupiter analogs.

To demonstrate how the HPIC can be used for surveys that are not solely focused on imaging Earth analogs, we simulate a 6 month survey to detect cool Jupiters using the LUVOIR-B-like design discussed in Section 3.1. For our "cool Jupiters," we adopt planets ranging from 6.0 to 14.3 Earth radii. We use an inner semimajor axis of 1.54 au, consistent with the "cool" temperature classification of Kopparapu et al. (2018), but constrain the outer semimajor axis to 5 au, scaling the semimajor axis constraints with stellar insolation. We again adopt the Dulz et al. (2020) occurrence rates, resulting in η_CJ = 0.32. To illustrate the potential magnitude of a target list for an alternative science goal, we do not require spectral characterization for these observations and focus only on V-band detections.

Table 3 summarizes the results of our calculation, and Figure 8 shows the population of stars selected for observation. The potential yield of this 6 month detection survey is 219 planets. The selected target list for this survey is much larger than that of the EEC survey, totaling more than 1100 stars and extending to fainter magnitudes and larger distances. Figure 8 shows that compared to the EEC survey, the target list shifts toward later-type stars. While the HZs of these stars are within the coronagraphic IWA and thus are not good targets for the EEC survey, they are acceptable direct imaging targets for the more distant cool planets. We note that the minimum completeness in Figure 8 is ∼50%. This therefore represents only a fraction of the stars that are potential cool Jupiter target stars; extension of the survey duration beyond 6 months could substantially expand the number of targets.

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